Learn about NumPy arrays which can be in many dimensions and are used as matrices. You can talk about creating arrays, using operators, reshaping and more. You can also learn the difference between NumPy arrays and classic algebra matrices.
- Let's take a look at NumPy arrays.…We already imported NumPy using input NumPy as np…so we can start using it right away.…Create an array arr equals np.array…and then one, two, and three.…And let's have a look at arr.…Let's see what we can do with this array.…For starters, it looks very much like a python list.…We can ask the length of the array,…we can access an element.…However, if we look closer, we'll see some differences.…For once, the type of the array elements…is a numpy int64 and not a python integer.…
When you create an array,…NumPy will determine the default type…for the array from the input.…So we can check the array data type it holds…by accessing the arr.dtype,…and you see that it's int64.…As a reminder, I'm hitting Shift + Enter…to run the set and advance to the next one.…So we can specify which data type…we want to use when creating the array.…Let's do arr32 equal np.array…with one, two, and three,…and also dtype…equal…np.int32.…
And then look at arr32,…and we see that the dtype now is int32.…We can multiply an array,…
- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
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